1. Field of the Invention
The present invention relates to a method for the identification of cotton contaminants with X-ray microtomographic image analysis.
2. Description of Related Art
As a natural fiber, cotton is subject to contamination from a variety of sources, including surrounding vegetation, insects, and materials involved in cotton harvesting and handling. The contaminants, including seed-coat fragments, bark, plastics and leaves, from these sources, which survive the ginning process, have a direct impact on the grade and, hence, the value of the cotton and its derivatives. It is at this stage in the manufacturing process that a precise measurement and identification of the cotton contaminants can improve the accuracy and repeatability of the grading operation. Furthermore, such measurements can provide the necessary feedback for optimizing both the production and the ginning processes—the latter is known to directly impact cotton's market value.
Because of the foregoing reasons, it is not surprising that the U.S. Department of Agriculture (USDA) has had a long-standing interest in the measurement of cotton contaminants in a cotton sample. This interest has in turn stimulated a significant amount of research in this area for more than six decades, leading to the development of a number of practical technologies. In general, such technologies can be categorized into two main groups: gravimetrics and surface scanners. Systems in the former group [e.g., the Shirley Analyzer and the Advanced Fiber Information System (AFIS)] accomplish their goal by separating and weighing or counting and sizing the contaminants. On the other hand, surface scanners [e.g., trashmeters of High-Volume Instruments (HVI)] capture an image of the sample surface and quantify its trash content by the subsequent analysis of that image. Recent research efforts for improving the sensitivity of these systems have generated only incremental improvements, including better separation machinery, use of color scanners and more sophisticated image analysis techniques, and more effective sample preparation mechanisms. However, despite these improvements, systems within both categories suffer from some fundamental limitations, some of which cannot be overcome. For example, gravimetric methods cannot distinguish between different trash particles. Surface scanners, which use visible or even near-infrared light for imaging, cannot penetrate the sample and, therefore, require sample preparation. Furthermore the results generated by these scanners will vary depending on the relative pose of the sample. Another fundamental limitation of all these systems is that of spatial resolution, which is currently in the 100's of microns.
The evolution of tomographic imaging dates back all the way to 1917, when an Austrian mathematician, Radon, showed that it was theoretically possible to reconstruct an object of arbitrary shape from its projections. It wasn't until 1972, however, that interest was generated in this field after the invention of the x-ray computed tomographic scanner by Hounsfield. He shared his discovery with Allan Cormack, who independently discovered some of the algorithms for image reconstruction. Hounsfield and Cormack showed that it is possible to compute high-quality, cross-sectional images with a high degree of accuracy from projections generated by passing x-rays through the object at different angles. Since then, an enormous amount of research has been conducted in the field of tomographic imaging. This has led to major improvements in the efficiency of the algorithms used in the reconstruction, the size of the volume that may be processed, the resolution, and more.
Fuzzy Logic was initiated in 1965 by Lotfi A. Zadeh, Professor of Systems Theory at the University of California, Berkeley. The most significant difference between fuzzy logic and conventional logic relates to the existence of fuzzy subsets. In conventional logic, the notion of a set of elements is based on the Law of the Excluded Middle, which states that an element is either a member of a set or not. This absence of a “middle ground” may be regarded as one of the flaws of conventional logic, one that is exploited by fuzzy logic. In fuzzy logic, an element may belong to any one of many subsets. The degree of belonging, however, should not be restricted to the values {0, 1} as in the case of conventional logic; rather, it may take any number of intermediate values, as defined by a particular function. In fuzzy logic terminology, this function (characteristic to a subset) is known as the membership function of that set. The membership function is a continuous (or piecewise continuous) function in [0, 1] and defines the degree to which an element belongs to a particular subset. In many everyday situations where conventional set representation fails, the concept of fuzzy membership functions may be applied to meaningfully describe abstract notions. Another important characteristic of fuzzy sets is that a member from the universe X may simultaneously be a member of several sets (indicated by the overlap of the membership functions).